Leveraging a big data model in the IT domain

Big Data Visibility with NPBs

Leveraging a big data model in the IT domain

What is big data?

Big data is high-volume, high-velocity and high-variety information

assets that demand cost-effective, innovative forms of information

processing for enhanced insight and decision making.

– Gartner

2© 2003-2014 VSS Monitoring. All rights reserved.

Network Data is a Big Data Problem

3.6Exabytes

Per Month by 2014

300xData Growth by 2020

70%Server Workloads

Virtualized by 2014

Performance

Management

Security &

Forensics

Business

Intelligence

Network data & packet metadata are required for:

Only 0.5%Network Packets Get Analyzed Outside of Operations


Today’s Network Analysis Architecture

� Storage is tightly coupled with analytics

application

� Data is stored in proprietary silos

� Data is replicated across different toolsets

� High CAPEX and OPEX

Problems


� Expanded analysis capabilities

� Storage scaling due to forensics

and compliance

� Greater efficiency and agility

Business Needs

Analytics

Processing

Storage

Solution: Decouple Storage from Processing & Analytics

Analytics

Processing

Storage


Early Analytics Model

� Collection, Storage and Presentation

functions are collapsed

� Probes are highly distributed


File creation

Storage

Processing and Retrieval Framework

Querying and

Info Generation

Indexing

Visualization and Reporting

� Analytics software tightly coupled

with hardware.

� Storage and Presentation layers are

collapsed.

� Probes are still distributed from

storage perspective.

Current Analytics Model

� Collection has been decoupled from

Storage and Presentation layers

� Capture is distributed, as proxy for

Analytics probes.

Capture and Grooming

File creation

Storage


Querying and

Info Generation

Indexing




Availability of cheap, accelerated HW has created a wedge between storage and presentation layer.

Big data technologies using distributed computing models (e.g. Hadoop) are scaling analytics to massive quantities of disparate data – beyond the capacities of a distributed storage model (probe-based) and are decoupling the Presentation / Analytics layer from the underlying data handling framework.

Purpose built HW and file creation functions can be offloaded to hardware based technologies, such as Network Packet Brokers.

Capture and Grooming

File creation

Storage


Querying and

Info Generation

Indexing

Next-Gen (big data) Analytics Model


Decoupling Software from Hardware


This is not a new story?

“There’s no army that can hold

back an economic principle

whose time has come.”

– John Donovan, AT&T SEVP of

Technology and Network Operations

Big Data Visibility Architecture

Capture & forward directly to network tools

and Big Data platforms

Network tools analyze network packets (real

time and historical). Data is typically stored

in small increments in a proprietary format.

The Big Data Architecture allows tools to be

virtualized and run on alternative computing

environments such as Hadoop.

Value Proposition

Capture & Groom Analyze & PresentIngest & StoreGenerate

Larger Datasets Optimized Data Lower Cost Faster Retrieval Total Visibility

Addresses

storage as

neglected third

pillar of visibility

Scales Network

Analytics and

Forensics

Provides Network

Visibility to Big

Data Systems


Decoupling storage from analytics, allowing network data to be stored and

analyzed by tools of choice—including big data technologies and virtualized

probes—significantly lowers the TCO of packet capture for forensics, compliance

and data mining.

Learn more5

“There are prodigious volumes of operational and business data available within network packet

streams, waiting to be fully leveraged by the fast growing ranks of Big Data analytics solutions.

By offering new methods for directly forwarding packet captures into Big Data architectures,

VSS Monitoring is opening a new door for operational-intelligence solutions.”

Jim Frey, Vice President of Research at Enterprise Management Associates

“By bringing together the Riverbed® Cascade® Shark continuous packet capture, analysis and

storage appliance with VSS Monitoring’s vBrokers powered by vSpool™, customers are able

to gain a new level of visibility, flexibility and control over their Big Data, resulting in significant

ROI and enhanced intelligence about their IT and network infrastructure.”

Dimitri Vlachos, Vice President, Marketing and Products at Riverbed

� Register for 2-Part Webinars (Live & Recorded):

� April 30, 2014 Leveraging a Big Data Model in the IT Domain,

Part 1 | Register: http://goo.gl/fCxUNK

� May 7, 2014 Considerations for Ramping to a Big Data

Network Monitoring Architecture, Part 2

Register: http://goo.gl/CTTv2S

� Read VSS Monitoring Press Release: http://goo.gl/OuZ1Q7

� Visit Big Data Visibility solution page (QR Code)
